Discriminating between direct and machine generated human voices

Abstract

Discriminating between direct and machine-generated human voices is disclosed. A directly-generated voice audio sample from a human utterance and a machine-generated voice audio sample outputted by a loudspeaker from a pre-recording of another human utterance are captured on a microphone. Discriminative features between the directly-generated voice audio sample and the machine-generated voice audio sample are extracted with a machine learning classifier. A response to a command in the captured directly-generated voice audio sample or the captured machine-generated voice audio sample may be selectively generated.

What is claimed is: 
     
         1 . A method for discriminating between direct and machine-generated human voices, the method comprising:
 capturing on a microphone a directly-generated voice audio sample from a human utterance;   capturing on the microphone a machine-generated voice audio sample outputted by a loudspeaker from a pre-recording of another human utterance;   extracting, with a machine learning feature extractor, discriminative features between the directly-generated voice audio sample and the machine-generated voice audio sample; and   selectively generating a response to a command in the captured directly-generated voice audio sample or the captured machine-generated voice audio sample.   
     
     
         2 . The method of  claim 1 , wherein the machine learning feature extractor is selected from a group consisting of: a multilayer perceptron (MCP), a convolutional neural network (CNN), and a recurrent neural network (RNN). 
     
     
         3 . The method of  claim 1 , further comprising:
 training the machine learning feature extractor with an audio sample classifier using a first class of voice data from audio captured directly from a human and a second class of voice data from audio captured from the loudspeaker.   
     
     
         4 . The method of  claim 3 , wherein training the machine learning feature extractor includes adding one or more types of noise signals to either or both the audio captured directly from a human and audio captures from the loudspeaker to enhance the machine learning feature extractor to operate over diverse environmental conditions. 
     
     
         5 . The method of  claim 1 , wherein one of the discriminative features of the machine-generated voice audio sample is a non-flat frequency response in an audible frequency band. 
     
     
         6 . The method of  claim 1 , wherein one of the discriminative features of the machine-generated voice audio sample is a ringing. 
     
     
         7 . The method of  claim 1 , wherein one of the discriminative features of the machine-generated voice audio sample is a vibration. 
     
     
         8 . The method of  claim 1 , wherein one of the discriminative features of the machine-generated voice audio sample is distortion. 
     
     
         9 . The method of  claim 1 , wherein on of the discriminative features of the machine-generated voice audio sample is added noise. 
     
     
         10 . The method of  claim 3 , wherein the machine learning feature extractor is trained using voice data from audio captured from a plurality of different loudspeakers, each having a unique set of sound reproduction characteristics. 
     
     
         11 . A system for discriminating between direct and machine-generated human voices, the system comprising:
 a microphone capturing both directly-generated voice audio samples from a human utterance and a machine-generated voice audio samples outputted by a loudspeaker from a pre-recording of the human utterance; and   a machine learning classifier receptive to the directly-generated voice audio samples and the machine-generated voice audio samples, the machine learning classifier deriving discriminative features between the directly-generated voice audio samples and the machine-generated voice audio samples and classifying as either directly generated or machine generated.   
     
     
         12 . The system of  claim 11 , wherein the machine learning classifier is selected from a group consisting of: a multilayer perceptron (MCP), a convolutional neural network (CNN), and a recurrent neural network (RNN). 
     
     
         13 . The system of  claim 11 , further comprising:
 a wake word detection module cooperating with the machine learning classifier.   
     
     
         14 . A system for discriminating between direct and machine-generated human voices, the system comprising:
 a microphone capturing both directly-generated voice audio samples from a human utterance and a machine-generated voice audio samples outputted by a loudspeaker from a pre-recording of the human utterance as input audio samples;   a machine learning classifier receptive to the input audio samples, the machine learning classifier deriving discriminative features between the directly-generated voice audio samples and the machine-generated voice audio samples and identifying the input audio samples as either directly generated or machine generated based upon the derived discriminative features;   a command processor connected to the machine learning classifier, the command processor selectively generating responses to commands in the input audio samples depending upon an activated one of operating modes.   
     
     
         15 . The system of  claim 14 , wherein one of the operating modes is a direct voice action mode in which the command processor generates a response to the command when the input audio sample is identified as a directly generated. 
     
     
         16 . The system of  claim 14 , wherein one of the operating modes is a machine generated voice action mode in which the command processor generates a response to the command when the input audio sample is identified as machine generated. 
     
     
         17 . The system of  claim 14 , wherein one of the operating modes is a hybrid action mode in which the command processor generates a response to the command when the input audio sample is identified as either directly generated or machine generated. 
     
     
         18 . The system of  claim 14 , further comprising:
 a user interface for selecting and configuring the operating modes.   
     
     
         19 . The system of  claim 14 , further comprising:
 an audio sample classifier training the machine learning classifier using a first class of voice data corresponding to directly-generated voice audio samples and a second class of voice data corresponding to machine0generated voice audio samples.   
     
     
         20 . The system of  claim 14 , wherein the machine learning classifier is selected from a group consisting of: a multilayer perceptron (MCP), a convolutional neural network (CNN), and a recurrent neural network (RNN).